Introduction to machine learning:
"Machine learning - a computer's ability to learn - is transforming our world: it is used to understand images, process text, make predictions by analyzing large amounts of data, and much more. It can be used in nearly every industry to improve efficiency and help stakeholders make better...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Champaign
Wolfram Media, Inc.
[2021]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Machine learning - a computer's ability to learn - is transforming our world: it is used to understand images, process text, make predictions by analyzing large amounts of data, and much more. It can be used in nearly every industry to improve efficiency and help stakeholders make better decisions. Whatever your industry or hobby, chances are that these modern artificial intelligence methods will be useful to you as well. "Introduction to Machine Learning" weaves reproducible coding examples into explanatory text to show what machine learning is, how it can be applied, and how it works. Perfect for anyone new to the world of AI or those looking to further their understanding, the text begins with a brief introduction to the Wolfram Language, the programming language used for the examples throughout the book. From there, readers are introduced to key concepts before exploring common methods and paradigms such as classification, regression, clustering, and deep learning. The math content is kept to a minimum to focus on what matters-applying the concepts in useful contexts. This book is sure to benefit anyone curious about the fascinating field of machine learning"-- |
| Beschreibung: | Includes index |
| Beschreibung: | xvi, 406 Seiten Illustrationen, Diagramme |
Internformat
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Datensatz im Suchindex
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|---|---|
| adam_text | Table of Contents Preface vii Short Introduction to the Wolfram Language xi 1 I What Is Machine Learning? 1 2 I Machine Learning Paradigms 9 Supervised Learning 9 · Unsupervised Learning 12 · Reinforcement Learning 15 · Other Learning Paradigms 17 3 I Classification 27 Carvs. Truck 27 · Titanic Survival 30 · Topic Classification 35 · Image Identification 40 · Classification Measures 45 · From Probabilities to Decisions 52 4 I Regression 61 Car Stopping Distances 61 · Brain Weights 65 · Boston Homes 68 · Regression Measures 73 5 I How It Works 81 Model 81 · Nonparametric Methods 83 · Parametric Methods 89 · Model Generalization 95 · Overfitting and Underfitting 97 · Regularization 103 · Hyperparameter Optimization 106 · Why Predictions Are Not Perfect 109 6 I Clustering 123 Fisher’s Irises 123 ■ Face Clustering 127 ■ News Aggregator 130 · DNA Hierarchical Clustering 133 7 I Dimensionality Reduction 139 Manifold Learning 139 · Data Visualization 145 · Search 149 · Anomaly Detection Denoising 151 ■ Missing Data Synthesis 153 · Autoencoder 154 · Recommendation 158 8 I Distribution Learning Univariate Data 165 · Fisher’s Irises 167 · Missing Data Synthesis 175 · Anomaly Detection 178 165
9 I Data Preprocessing 183 Preprocessing Pipeline 183 · Numeric Data 184 · Categorical Data 188 · Image 191 · Text 196 10 I Classic Supervised Learning Methods 211 Illustrative Examples 211 ■ Linear Regression 213 · Logistic Regression 217 · Nearest Neighbors 222 · Decision Tree 227 · Random Forest 231 ■ Gradient Boosted Trees 236 ■ Support-Vector Machine 242 · Gaussian Process 247 · Markov Model 259 11 I Deep Learning Methods 271 From Neurons to Networks 271 ■ How Neural Networks Learn 284 · Convolutional Networks 302 ■ Recurrent Networks 324 · Transformer Networks 348 12 I Bayesian Inference 379 Coin Flip Experiment 379 · Bayesian Inference 382 ■ Bayesian Learning for Predictive Modeling385 ■ Probabilistic Programming 395 Going Further 401 Index 403
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| adam_txt |
Table of Contents Preface vii Short Introduction to the Wolfram Language xi 1 I What Is Machine Learning? 1 2 I Machine Learning Paradigms 9 Supervised Learning 9 · Unsupervised Learning 12 · Reinforcement Learning 15 · Other Learning Paradigms 17 3 I Classification 27 Carvs. Truck 27 · Titanic Survival 30 · Topic Classification 35 · Image Identification 40 · Classification Measures 45 · From Probabilities to Decisions 52 4 I Regression 61 Car Stopping Distances 61 · Brain Weights 65 · Boston Homes 68 · Regression Measures 73 5 I How It Works 81 Model 81 · Nonparametric Methods 83 · Parametric Methods 89 · Model Generalization 95 · Overfitting and Underfitting 97 · Regularization 103 · Hyperparameter Optimization 106 · Why Predictions Are Not Perfect 109 6 I Clustering 123 Fisher’s Irises 123 ■ Face Clustering 127 ■ News Aggregator 130 · DNA Hierarchical Clustering 133 7 I Dimensionality Reduction 139 Manifold Learning 139 · Data Visualization 145 · Search 149 · Anomaly Detection Denoising 151 ■ Missing Data Synthesis 153 · Autoencoder 154 · Recommendation 158 8 I Distribution Learning Univariate Data 165 · Fisher’s Irises 167 · Missing Data Synthesis 175 · Anomaly Detection 178 165
9 I Data Preprocessing 183 Preprocessing Pipeline 183 · Numeric Data 184 · Categorical Data 188 · Image 191 · Text 196 10 I Classic Supervised Learning Methods 211 Illustrative Examples 211 ■ Linear Regression 213 · Logistic Regression 217 · Nearest Neighbors 222 · Decision Tree 227 · Random Forest 231 ■ Gradient Boosted Trees 236 ■ Support-Vector Machine 242 · Gaussian Process 247 · Markov Model 259 11 I Deep Learning Methods 271 From Neurons to Networks 271 ■ How Neural Networks Learn 284 · Convolutional Networks 302 ■ Recurrent Networks 324 · Transformer Networks 348 12 I Bayesian Inference 379 Coin Flip Experiment 379 · Bayesian Inference 382 ■ Bayesian Learning for Predictive Modeling385 ■ Probabilistic Programming 395 Going Further 401 Index 403 |
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| author | Bernard, Étienne ca. 20./21. Jh |
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| author_facet | Bernard, Étienne ca. 20./21. Jh |
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| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
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| spelling | Bernard, Étienne ca. 20./21. Jh. Verfasser (DE-588)126225096X aut Introduction to machine learning Etienne Bernard Champaign Wolfram Media, Inc. [2021] xvi, 406 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Includes index "Machine learning - a computer's ability to learn - is transforming our world: it is used to understand images, process text, make predictions by analyzing large amounts of data, and much more. It can be used in nearly every industry to improve efficiency and help stakeholders make better decisions. Whatever your industry or hobby, chances are that these modern artificial intelligence methods will be useful to you as well. "Introduction to Machine Learning" weaves reproducible coding examples into explanatory text to show what machine learning is, how it can be applied, and how it works. Perfect for anyone new to the world of AI or those looking to further their understanding, the text begins with a brief introduction to the Wolfram Language, the programming language used for the examples throughout the book. From there, readers are introduced to key concepts before exploring common methods and paradigms such as classification, regression, clustering, and deep learning. The math content is kept to a minimum to focus on what matters-applying the concepts in useful contexts. This book is sure to benefit anyone curious about the fascinating field of machine learning"-- Machine learning Machine learning fast Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Wolfram Programmiersprache (DE-588)1091597596 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Wolfram Programmiersprache (DE-588)1091597596 s DE-604 Online version Bernard, Etienne Introduction to machine learning Champaign : Wolfram Media, Inc., [2021] Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033590573&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bernard, Étienne ca. 20./21. Jh Introduction to machine learning Machine learning Machine learning fast Maschinelles Lernen (DE-588)4193754-5 gnd Wolfram Programmiersprache (DE-588)1091597596 gnd |
| subject_GND | (DE-588)4193754-5 (DE-588)1091597596 |
| title | Introduction to machine learning |
| title_auth | Introduction to machine learning |
| title_exact_search | Introduction to machine learning |
| title_exact_search_txtP | Introduction to machine learning |
| title_full | Introduction to machine learning Etienne Bernard |
| title_fullStr | Introduction to machine learning Etienne Bernard |
| title_full_unstemmed | Introduction to machine learning Etienne Bernard |
| title_short | Introduction to machine learning |
| title_sort | introduction to machine learning |
| topic | Machine learning Machine learning fast Maschinelles Lernen (DE-588)4193754-5 gnd Wolfram Programmiersprache (DE-588)1091597596 gnd |
| topic_facet | Machine learning Maschinelles Lernen Wolfram Programmiersprache |
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